270 citations · 610 across the 22 of their papers we have counts for
6 papers · 1 filter
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation
Zelin Zang, Hao Luo, Kai Wang +4
Unsupervised Contrastive learning has gained prominence in fields such as vision, and biology, leveraging predefined positive/negative samples for representation learning. Data aug…
LookHops: light multi-order convolution and pooling for graph classification
Zhangyang Gao, Haitao Lin, Stan. Z Li
Convolution and pooling are the key operations to learn hierarchical representation for graph classification, where more expressive -order() method requires more computatio…
Consistent Representation Learning for High Dimensional Data Analysis
Stan Z. Li, Lirong Wu, Zelin Zang
High dimensional data analysis for exploration and discovery includes three fundamental tasks: dimensionality reduction, clustering, and visualization. When the three associated ta…
Invertible Manifold Learning for Dimension Reduction
Siyuan Li, Haitao Lin, Zelin Zang +3
Dimension reduction (DR) aims to learn low-dimensional representations of high-dimensional data with the preservation of essential information. In the context of manifold learning,…
Clustering Based on Graph of Density Topology
Zhangyang Gao, Haitao Lin, Stan. Z Li
Data clustering with uneven distribution in high level noise is challenging. Currently, HDBSCAN is considered as the SOTA algorithm for this problem. In this paper, we propose a no…
Markov-Lipschitz Deep Learning
Stan Z. Li, Zelin Zang, Lirong Wu
We propose a novel framework, called Markov-Lipschitz deep learning (MLDL), to tackle geometric deterioration caused by collapse, twisting, or crossing in vector-based neural netwo…